1. Field of the Invention
The present invention relates to the field of user interface design and analysis of human factors which are considered pertinent during the development stages of the user interface. In particular, this invention considers human factors, through behavioral modeling methods, and then incorporates such factors into the iterative design stage of interface development.
2. Description of Background Information
The traditional view of user performance during interface design and testing is that variability in responses, preferences, and behavior reflects poor design. The common knowledge and practice in the industry is to represent the user population as having a single set of characteristics and behaviors. In current practice, this single set of characteristics and behaviors focuses on only one of three types: expert, novice, or composite. One group is represented to the exclusion of other groups' needs. This is a particularly inappropriate method of designing in that there is a substantial risk that very few users will be best accommodated by the interface. Subsequently, an interface is designed in such a way that variability would be reduced. As a consequence, the diversity of the user population is neglected and users' unique needs and preferences are effectively ignored.
The common knowledge and practice in the industry is twofold. First, it is common practice to take a single view of a user population, and second, to subsequently design system interfaces based on this view. For example, a system interface may be designed to accommodate the behavior of an expert user (e.g., customer service and sales representatives). Alternatively, interfaces can be designed to accommodate a novice user (e.g., interfaces used in automated teller machines for use by the general public). Thus, the current practice represents the user population with a single set of characteristics and behaviors. If users or agents are categorized in any way, they are done so in an informal manner, based primarily on the opinion and judgement of local operating management and not based on formal qualitative and quantitative models, statistical data, or similar objective empirical measures.
Since it is common practice to take a singular view of the user population, the interface is designed and tested to reflect average or prototypical end user performance. For instance, during usability testing it is typical to deem a workflow task or design implementation a failure if 5 of 10 users successfully perform the task or function even though the interface was designed superbly for 5 of the users. Similarly, a design implementation is commonly deemed acceptable it 10 of 10 users performed adequately even though a closer examination may reveal that the majority of users reflected outstanding performance while the remaining subset could not display the required behavior. In both of these examples, the variability or diversity in performance is not considered during design or testing. Distinctive behaviors that may be desirable are not tracked, captured, or accommodated since the emphasis has commonly focused on accommodating average behavior. The testing and design phase of interface development does not capitalize upon, or accommodate, variability in performance primarily because management and systems engineers typically accept the singular view of one user-representation.
Capturing the behavioral diversity of the user population is the first of two necessary steps toward the design and deployment of systems and processes that accommodate the specific needs of the user (agent) and facilitate business goals. The second necessary step is systematically integrating the agent models to the design and engineering of user interfaces.
Traditionally, the diversity of a user population has not been taken into account during the iterative design stage of interface development. Rather, a system is typically designed with the simplistic view of the “average” or prototypical user in mind. This approach does not accommodate the entire range of behaviors and characteristics of the user population. This single-view may hinder performance of a large proportion of users, given that their specific needs are not accommodated and management and systems interface engineers are unable to capitalize on the unique behavioral qualities that could facilitate performance and achieve business goals.
A solution to this approach is to consider the range of behavioral characteristics of the entire user population during the design phase of interface development. This broad range of behavior is ideally captured through use of behavioral models. Once the user population is categorized into a reasonable number of groups, the resultant qualitative and quantitative models can be integrated into system design and testing.
Prior art which discloses behavioral models are U.S. patent application Ser. No. 09/089,403, filed on Jun. 3, 1998, entitled “A Method for Categorizing, Describing, and Modeling Types of Systems Users” and provisional U.S. Patent Application No. 60/097,174, filed on Aug. 20, 1998, entitled “A Method for Intelligent Call Routing Utilizing a Performance Optimizing Calculation Integrating Customer and Agent Behavioral Models”.
The Categorize Describe-Model (CDM) methodology, disclosed in U.S. patent application Ser. No. 09/089,403, is a technique used to categorize a diverse user population into a reasonable number of groups that share similar characteristics. The behaviors of users within these groups are then objectively described and subsequently quantitatively and qualitatively modeled. At any point in this process, the grouping characteristics may be validated and revised based on the data collected and modifications of bottom-line business goals. The end result of the CDM method is that a highly diverse user population is divided into a small number of behaviorally distinctive groups (e.g., 3–5 user-groups). The members of each group share similar characteristics and behaviors. In effect, by using the CDM methodology, the entire range of behavioral diversity of a user population can be captured and accommodated.